T0 samples parameter plots

Coloured points are individual samples with the black cross as the mean with the 95% CI

Comparing T0 samples with T90 samples

<<<<<<< HEAD

Points are the mean with the bars representing standard error

Total Protein Model Estimates and 95% CI

Carbohydrate Model Estimates and 95% CI

Symbiont Density Model Estimates and 95% CI

Chlorophyll a Model Estimates and 95% CI

Colour Intensity Model Estimates and 95% CI

=======

Points are the mean with the bars representing standard error

Model estimate 95% confidence interval for physiologies

>>>>>>> 14853594f80c2e3a3218ded1c5c998fe5816dabf

T90 plot of all parameters

Plotting physiology

Plotting sum colour intensity metrics
<<<<<<< HEAD

=======

>>>>>>> 14853594f80c2e3a3218ded1c5c998fe5816dabf

Preliminary statistics (basic linear model and anova)

SSID linear models p-values
Carbohydrate Protein Symbiont Density Chlorophyll a Colour Intensity
Temperature 0.0022 0.7612 0.1781 0.0063 0.0405
280 pCO2 0.6774 0.7228 0.7211 0.5132 0.2808
700 pCO2 0.2133 0.3481 0.1362 0.0067 0.2041
2800 pCO2 0.8782 0.2430 0.0001 0.0000 0.0004
Reef Environment 0.6175 0.2905 0.2580 0.1641 0.0011
Temperature x Reef NA 0.0660 0.0540 NA NA
PSTR linear models p-values
Carbohydrate Protein Symbiont Density Chlorophyll a Colour Intensity
Temperature 0.0006 0.0001 0.0005 0.0002 0.0000
280 pCO2 0.4235 0.1194 0.8763 0.0645 0.6704
700 pCO2 0.3631 0.7584 0.1746 0.1886 0.2143
2800 pCO2 0.6688 0.9277 0.0973 0.1155 0.3556
Reef Environment 0.1361 0.5369 0.0001 0.0053 0.0387
Temperature x Reef NA NA 0.0424 NA 0.0367
PAST linear models p-values
Carbohydrate Protein Symbiont Density Chlorophyll a Colour Intensity
Temperature 0.0492 0.0739 0.4838 0.0359 0.0004
280 pCO2 0.6023 0.4606 0.5614 0.6830 0.0523
700 pCO2 0.0196 0.1696 0.2129 0.0058 0.0046
2800 pCO2 0.0121 0.0099 0.5500 0.0008 0.1385
Reef Environment 0.1508 0.2345 0.5783 0.8997 0.4500
Temperature x 280 pCO2 NA NA NA 1.1949138702567e-09 NA
Temperature x 700 pCO2 NA NA NA 2.13430417166503e-05 NA
Temperature x 2800 pCO2 NA NA NA 0.231211071936069 NA
UTEN linear models p-values
Carbohydrate Protein Symbiont Density Chlorophyll a Colour Intensity
Temperature 0.3819 0.5284 0.3287 0.0058 0.0000
280 pCO2 0.2115 0.4150 0.6347 0.4250 0.8840
700 pCO2 0.6568 0.5509 0.3582 0.2885 0.0150
2800 pCO2 0.8366 0.7393 0.3587 0.7004 0.0535
Reef Environment 0.6180 0.9055 0.3112 0.4654 0.0586

T90 plot of all parameters by reefzone

<<<<<<< HEAD

=======

>>>>>>> 14853594f80c2e3a3218ded1c5c998fe5816dabf

PCA of physiology at T90

Comparison between physiological parameters per fragment

Linear Regression between carbohydrate and protein

## 
## Call:
## lm(formula = pro ~ carb, data = df.90b)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.68459 -0.12391 -0.04553  0.09898  0.60632 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.10883    0.01890   5.757 2.01e-08 ***
## carb         0.28467    0.02132  13.350  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.187 on 320 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3577, Adjusted R-squared:  0.3557 
## F-statistic: 178.2 on 1 and 320 DF,  p-value: < 2.2e-16
Linear Regression between chlorophyll and symbiont density

## 
## Call:
## lm(formula = chla ~ den, data = df.90b)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -134.15  -43.09  -20.60   26.54  479.49 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   59.291      5.221  11.357  < 2e-16 ***
## den            5.959      1.361   4.378 1.63e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 69.35 on 319 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.05667,    Adjusted R-squared:  0.05371 
## F-statistic: 19.16 on 1 and 319 DF,  p-value: 1.628e-05
Linear Regression between colour intensity and symbiont density

## 
## Call:
## lm(formula = sum_bw5 ~ den, data = df.90b)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -360.06  -77.49   28.33  105.07  185.65 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -239.8862    10.3759 -23.120   <2e-16 ***
## den            0.2194     2.5581   0.086    0.932    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 128.7 on 276 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  2.666e-05,  Adjusted R-squared:  -0.003596 
## F-statistic: 0.007358 on 1 and 276 DF,  p-value: 0.9317
Linear Regression between colour intensity and chlorophyll

## 
## Call:
## lm(formula = sum_bw5 ~ chla, data = df.90b)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -314.37  -70.34   24.01  100.32  189.37 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -291.27978   10.31268 -28.245  < 2e-16 ***
## chla           0.66159    0.09593   6.897 3.58e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 118.9 on 278 degrees of freedom
##   (43 observations deleted due to missingness)
## Multiple R-squared:  0.1461, Adjusted R-squared:  0.143 
## F-statistic: 47.57 on 1 and 278 DF,  p-value: 3.581e-11
Linear Regression between protein and chlorophyll

## 
## Call:
## lm(formula = pro ~ chla, data = df.90b)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.84382 -0.15670 -0.01467  0.13149  0.66354 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.1955664  0.0161970   12.07   <2e-16 ***
## chla        0.0016608  0.0001573   10.56   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2007 on 321 degrees of freedom
## Multiple R-squared:  0.2578, Adjusted R-squared:  0.2555 
## F-statistic: 111.5 on 1 and 321 DF,  p-value: < 2.2e-16
Linear Regression between carbohydrate and chlorophyll

## 
## Call:
## lm(formula = carb ~ chla, data = df.90b)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9398 -0.3286 -0.1149  0.2597  2.2662 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.5493115  0.0367617  14.943  < 2e-16 ***
## chla        0.0025514  0.0003567   7.152 5.84e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4552 on 320 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1378, Adjusted R-squared:  0.1351 
## F-statistic: 51.16 on 1 and 320 DF,  p-value: 5.839e-12
Linear Regression between protein and calcification rate

## 
## Call:
## lm(formula = pro ~ rate, data = df.90b)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.61621 -0.14542 -0.04558  0.11987  0.68780 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.24088    0.01288   18.70   <2e-16 ***
## rate         0.17596    0.01402   12.55   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1939 on 278 degrees of freedom
##   (43 observations deleted due to missingness)
## Multiple R-squared:  0.3618, Adjusted R-squared:  0.3595 
## F-statistic: 157.6 on 1 and 278 DF,  p-value: < 2.2e-16
Linear Regression between carbohydrate and calcification rate

## 
## Call:
## lm(formula = carb ~ rate, data = df.90b)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.07630 -0.28281 -0.05503  0.16522  2.08199 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.59443    0.02932  20.273  < 2e-16 ***
## rate         0.23129    0.03190   7.251 4.15e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.441 on 277 degrees of freedom
##   (44 observations deleted due to missingness)
## Multiple R-squared:  0.1595, Adjusted R-squared:  0.1565 
## F-statistic: 52.58 on 1 and 277 DF,  p-value: 4.145e-12
Linear Regression between chlorophyll and calcification rate

## 
## Call:
## lm(formula = chla ~ rate, data = df.90b)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -157.09  -37.94  -11.42   23.36  417.72 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   58.027      4.112   14.11   <2e-16 ***
## rate          49.589      4.476   11.08   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 61.91 on 278 degrees of freedom
##   (43 observations deleted due to missingness)
## Multiple R-squared:  0.3063, Adjusted R-squared:  0.3038 
## F-statistic: 122.8 on 1 and 278 DF,  p-value: < 2.2e-16
Linear Regression between symbiont density and calcification rate

## 
## Call:
## lm(formula = den ~ rate, data = df.90b)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7465 -1.9950 -0.8537  0.6905 19.5315 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   2.6506     0.2023  13.103   <2e-16 ***
## rate          0.1482     0.2196   0.675      0.5    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.025 on 276 degrees of freedom
##   (45 observations deleted due to missingness)
## Multiple R-squared:  0.001647,   Adjusted R-squared:  -0.00197 
## F-statistic: 0.4553 on 1 and 276 DF,  p-value: 0.5004